Amel Ben Slimane Rahmouni

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This paper presents a robust algorithm for glottal closure instants (GCIs) detection of speech signals. The algorithm uses a multi-scale analysis based on a dyadic wavelet filterbank. Significant minima and maxima of the filtered signals are localized at each scale using adaptive mathematical morphology transformation of erosion. With reference to the GCIs(More)
Usable speech criteria are proposed to extract minimally corrupted speech for speaker identification (SID) in co-channel speech. In co-channel speech, either speaker can randomly appear as the stronger speaker or the weaker one at a time. Hence, the extracted usable segments are separated in time and need to be organized into speaker streams for SID. In(More)
Many applications of speech communication and speaker identification suffer from the problem of co-channel speech. This paper deals with a multi-resolution dyadic wavelet transform method for usable segments of co-channel speech detection that could be processed by a speaker identification system. Evaluation of this method is performed on TIMIT database(More)
A multi-scale analysis method, called Empirical Mode Decomposition (EMD), has been proposed for analysis of nonlinear and non stationary data. The empirical mode decomposition is a method initiated by Huang et al. as an alternative technique to the traditional Fourier and wavelet techniques for examining signals. It decomposes a signal into several(More)
Many applications of speech communication and speaker identification suffer from the problem of co-channel speech. This paper deals with a multi-resolution dyadic wavelet transform method for usable segments of co-channel speech detection that could be processed by a speaker identification system. Evaluation of this method is performed on TIMIT database(More)
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